search for: startvals

I am using both nlminb and optim to get MLEs from a likelihood function I have developed. AFAIK, the model I has not been previously used in this way and so I am struggling a bit to unit test my code since I don't have another data set to compare this kind of estimation to. The likelihood I have is (in tex below) \begin{equation} \label{eqn:marginal} L(\beta) = \prod_{s=1}^N \int

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

...189981, 2.098671, 2.122207, 2.012621, 1.963610, 1.884184, 1.955160, 1.801175, 1.829686, 1.773260, 1.588768, 1.563774, 1.559192) tpoints <- c(0,0,0,2,2,2,4,4,4,6,6,6,8,8,8,12,12,12,14,14,14,16,16,16,18,18,18,20,20,20,24,24,24) shift=mean(gene_expression) # y-axis (expression) shift # Perfect fit startvals <- list(phase=pi, amp=0.5) sine_nls <- nls(gene_expression ~ sin(tpoints * afreq + phase) * amp + shift, start=startvals, algorithm="port", lower=lowervals, upper=uppervals) # Convergence failure startvals <- list(phase=0, amp=0.5) sine_nls <- nls(gene_expression ~ sin(tpoint...

dear list, as a part my problem. I have to estimate some parameters using ML estimation. The form of the likelihood function is not straight forward and I had to use a for loop to define the function. I used "optim" to maximise the result but was not sure of the programme. To validate my results, I tried to write a function to obtain the MLE of a bivariate normal in the same manner. On

...I solve the following simple example? x^2 - y^2 = 6 x ? y = 3 I heard about nlsystemfit, but I don?t know how to run it exactly. I have tried the following code, but it doesn?t really work: f1 <-y~ x[1]^2-x[2]^2-6 f2 <-z~ x[1]-x[2]-3 f <- list(f1=0,f2=0) nlsystemfit("OLS",f,startvals=c(0,0)) Thank You in advance for your help. Evgeniq Petrova

Hello I'm new to R (one week) so please excuse any obvious mistakes in my code or posting. I am attempting to fit a non linear function defining the relationship between dependent variable A and the variables PAR and T grouped by the condition Di. The following steps are taken in the Rcode below: 1) load the data (not shown) 2) define the function to be fit 3) define the starting values

Hello all, I wanted to get some feedback on this patch for ScalarEvolution. It addresses a performance problem I am seeing for simple benchmark. Starting with this C code: 01: signed char foo(void) 02: { 03: const int count = 8000; 04: signed char result = 0; 05: int j; 06: 07: for (j = 0; j < count; ++j) { 08: result += (result_t)(3); 09: } 10: 11: return result; 12: } I

Hi everyone, I am new to R and keep getting the message Error in as.vector(x, mode) while trying to run nlsystemfit. Below is my exact code. The data is in Stata format because I only recently swapped to R and am trying to compare results from Stata to make sure I know what is going on. I have searched google and read sever R-help articles with this error. They all say the problem is to do

...p(thetareal, c(FALSE, o[ss, ])) 8: emfred(x, thetaold, indx$o, indx$m, indx$ivector, indx$icap, indx$AMr1, indx$AMr2, AM1stln = AM1stln, returntype = "theta", priors = priors, empri = empri, collect = collect) 9: emarch(x.stacked$x, p2s = p2s, thetaold = NULL, tolerance = tolerance, startvals = startvals, x.stacked$priors, empri = empri, frontend = frontend, collect = collect, autopri = prepped$autopri, emburn = emburn) 10: amelia.default(datM, m = 5, idvars = "group") 11: amelia(datM, m = 5, idvars = "group") Warning: stack imbalance in '<-', 108 then...

Dear R-users I'm running a maximum likelihood procedure using the spg package. I'd like to save some output produced in each iteration to a file, but if I put the capture.output() within the function I get the following message; Error in spg(par = startval, fn = loglik, gr = NULL, method = 3, lower = lo, : Failure in initial function evaluation!Error in -fn(par, ...) : invalid argument

I am trying to it a particular nonlinear model common in Soil Science to moisture release data from soil. I have written the function as shown below according to the logist example in Ch8 of Pinheiro & Bates. I am getting the following error (R version 2.1.1) *Error in qr(attr(rhs, "gradient")) : NA/NaN/Inf in foreign function call (arg 1)* Below is the function and data. /#

...2 1 1 2 1 1 1 2 ... $ os : num 0 1 0 0 1 1 1 0 0 1 ... $ css : num 0 1 0 0 1 1 1 0 0 1 ... $ rfs : num 0 1 1 0 1 1 1 0 0 1 ... $ comp : num 0 1 1 0 1 1 1 0 0 1 ... > set.seed(200) > M <- 50 # Number of imputations > am.imp <- amelia(utt.mi, m=M, p2s=1, startvals=1, write.out=F, + idvars=c('os','css','rfs','comp'), + noms=c('gender','symptoms','site','multifoc','ctnm','prebca','precystec' , + 'smk','surgery','ptnm.t','nodes','grade...

On Sun, Apr 11, 2010 at 4:09 PM, Jeffrey Yasskin <jyasskin at google.com> wrote: > Kenneth Uildriks <kennethuil at gmail.com> wrote: >> As I see it, the context switching mechanism itself needs to know >> where to point the stack register when switching.  The C routines take >> an initial stack pointer when creating the context, and keep track of >> it from

...e operation to test to see if the column is all zeros and tally up a character vector with the names of the columns to obtain the terms in equation i eqn.terms <- vector() for( v in 1:length( est$estimate ) ) { j <- attr( eval( deriv( as.formula( eqns[[i]] ), names( startvals ) ) ), "gradient" ) if( qr( j[,v] )$rank > 0 ) { eqn.terms <- rbind( eqn.terms, name <- names( est$estimate )[v] ) } } derivs[[i]] <- deriv( as.formula( eqns[[i]] ), eqn.terms, hessian=T )...

...etach(package: Hmisc) --------------- After examining the missing data patterns, impute 10 datasets using the amelia function from the Amelia package. Check the densities of the continuous variables to make sure they make sense. --------------- library(Amelia) am.imp <- amelia(d, m=10, p2s=1, startvals=1, write.out=F, noms=c('status','sex','hepmeg','trt')) compare.density(data=d, output=am.imp, var='trig') compare.density(data=d, output=am.imp, var='platelet') --------------- Since everything looks ok, fit Cox models to each of the 10 imputed data...

Kenneth Uildriks <kennethuil at gmail.com> wrote: > As I see it, the context switching mechanism itself needs to know > where to point the stack register when switching.  The C routines take > an initial stack pointer when creating the context, and keep track of > it from there.  If we don't actually need to interoperate with > contexts created from the C routines, we have

+CC llvm-dev On Tue, May 8, 2018 at 2:34 AM, Gal Zohar <Gal.Zohar at ceva-dsp.com> wrote: > I noticed that SCEV, when trying to perform strength reduction, doesn’t use > the ability to prove an induction variable does not signed/unsigned wrap due > to infinite loops. > > Is there an easy way to use the isAddRecNeverPoison function when > determining if strength reduction

Hi, I'm trying to fit some data using a logistic function defined as y ~ a * (1+m*exp(-x/tau)) / (1+n*exp(-x/tau) My data is below: x <- 1:100 y <- c(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,1,1,1,2,2,2,2,2,3,4,4,4,5, 5,5,5,6,6,6,6,6,8,8,9,9,10,13,14,16,19,21, 24,28,33,40,42,44,50,54,69,70,93,96,110,127,127,141,157,169,